Hands-on Exercise 2

Author

HTC

Published

September 3, 2025

Modified

September 4, 2025

4.1 Overview

This is hands-on Exercise 2 , 4.1

Getting started

pacman::p_load(sf, terra, spatstat, 
               tmap, rvest, tidyverse)
setwd("C:/Users/HT Chen/Desktop/ISSS626/ISSS626-wp/Hands-on_Ex/Hands-on_Ex02")
mpsz_sf <- st_read("data/geospatial/MasterPlan2019SubzoneBoundaryNoSeaKML (1).kml") %>% 
  st_zm(drop = TRUE, what = "ZM") %>% st_transform(crs = 3414)
Reading layer `URA_MP19_SUBZONE_NO_SEA_PL' from data source 
  `C:\Users\HT Chen\Desktop\ISSS626\ISSS626-wp\Hands-on_Ex\Hands-on_Ex02\data\geospatial\MasterPlan2019SubzoneBoundaryNoSeaKML (1).kml' 
  using driver `KML'
Simple feature collection with 332 features and 2 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 103.6057 ymin: 1.158699 xmax: 104.0885 ymax: 1.470775
Geodetic CRS:  WGS 84
extract_kml_field <- function(html_text, field_name) {
  if (is.na(html_text) || html_text == "") return(NA_character_)
  
  page <- read_html(html_text)
  rows <- page %>% html_elements("tr")
  
  value <- rows %>%
    keep(~ html_text2(html_element(.x, "th")) == field_name) %>%
    html_element("td") %>%
    html_text2()
  
  if (length(value) == 0) NA_character_ else value
}
mpsz_sf <- mpsz_sf %>%
  mutate(
    REGION_N = map_chr(Description, extract_kml_field, "REGION_N"),
    PLN_AREA_N = map_chr(Description, extract_kml_field, "PLN_AREA_N"),
    SUBZONE_N = map_chr(Description, extract_kml_field, "SUBZONE_N"),
    SUBZONE_C = map_chr(Description, extract_kml_field, "SUBZONE_C")
  ) %>%
  select(-Name, -Description) %>%
  relocate(geometry, .after = last_col())
mpsz_cl <- mpsz_sf %>%
  filter(SUBZONE_N != "SOUTHERN GROUP",
         PLN_AREA_N != "WESTERN ISLANDS",
         PLN_AREA_N != "NORTH-EASTERN ISLANDS")
setwd("C:/Users/HT Chen/Desktop/ISSS626/ISSS626-wp/Hands-on_Ex/Hands-on_Ex02")
write_rds(mpsz_cl, "data/mpsz_cl.rds")
setwd("C:/Users/HT Chen/Desktop/ISSS626/ISSS626-wp/Hands-on_Ex/Hands-on_Ex02")
childcare_sf <- st_read("data/geospatial/ChildCareServices.kml") %>% 
  st_zm(drop = TRUE, what = "ZM") %>%
  st_transform(crs = 3414)
Reading layer `CHILDCARE' from data source 
  `C:\Users\HT Chen\Desktop\ISSS626\ISSS626-wp\Hands-on_Ex\Hands-on_Ex02\data\geospatial\ChildCareServices.kml' 
  using driver `KML'
Simple feature collection with 1925 features and 2 fields
Geometry type: POINT
Dimension:     XYZ
Bounding box:  xmin: 103.6878 ymin: 1.247759 xmax: 103.9897 ymax: 1.462134
z_range:       zmin: 0 zmax: 0
Geodetic CRS:  WGS 84
plot(st_geometry(mpsz_cl))
plot(st_geometry(childcare_sf), add = TRUE)

tmap_mode('view')
tm_shape(childcare_sf)+
  tm_dots()
tmap_mode('plot')
childcare_ppp <- as.ppp(childcare_sf)
class(childcare_ppp)
[1] "ppp"
summary(childcare_ppp)
Marked planar point pattern:  1925 points
Average intensity 2.417323e-06 points per square unit

Coordinates are given to 11 decimal places

Mark variables: Name, Description
Summary:
     Name           Description       
 Length:1925        Length:1925       
 Class :character   Class :character  
 Mode  :character   Mode  :character  

Window: rectangle = [11810.03, 45404.24] x [25596.33, 49300.88] units
                    (33590 x 23700 units)
Window area = 796335000 square units
sg_owin <- as.owin(mpsz_cl)
class(sg_owin)
[1] "owin"
plot(sg_owin)

childcareSG_ppp = childcare_ppp[sg_owin]
childcareSG_ppp
Marked planar point pattern: 1925 points
Mark variables: Name, Description 
window: polygonal boundary
enclosing rectangle: [2667.54, 55941.94] x [21448.47, 50256.33] units
#without csr
clarkevans.test(childcareSG_ppp,
                correction="none",
                clipregion="sg_owin",
                alternative=c("clustered"))

    Clark-Evans test
    No edge correction
    Z-test

data:  childcareSG_ppp
R = 0.53532, p-value < 2.2e-16
alternative hypothesis: clustered (R < 1)
#with csr
clarkevans.test(childcareSG_ppp,
                correction="none",
                clipregion="sg_owin",
                alternative=c("clustered"),
                method="MonteCarlo",
                nsim=99)

    Clark-Evans test
    No edge correction
    Monte Carlo test based on 99 simulations of CSR with fixed n

data:  childcareSG_ppp
R = 0.53532, p-value = 0.01
alternative hypothesis: clustered (R < 1)
  • Without CSR:

  • Test used:

    Clark–Evans nearest-neighbor test (no edge correction), alternative = “clustered”.

    Result: R=0.5353, R = 0.5353 with p-value < 2.2×10⁻¹⁶.

    Interpretation: R<1R<1R<1 and the p-value is far below 0.05 ⇒ reject H0H_0H0​ (Complete Spatial Randomness).
    The observed average nearest-neighbor distance is ~53.5% of what we’d expect under CSR, indicating strong, statistically significant clustering of childcare locations.

  • Business communication:

    Childcare centres are not randomly spread out; they cluster in a few hubs.

    This implies over-concentration in certain neighbourhoods and potential service gaps elsewhere.

    Actions:

    • Prioritise licensing/incentives for new centres in under-served subzones between/away from current clusters to improve equitable access.
  • With CSR(Monte Carlo)

  • Test used:

    More realistic for irregular/finite windows; typically more conservative.

    Result: R = 0.535, p = 0.01,

    Decision: Reject H₀ at α = 0.05. Childcare locations are significantly clustered

  • Business communication:

    Childcare centres are concentrated in a few neighbourhoods rather than evenly spread.

kde_SG_diggle <- density(
  childcareSG_ppp,
  sigma=bw.diggle,
  edge=TRUE,
  kernel="gaussian") 
plot(kde_SG_diggle)

summary(kde_SG_diggle)
real-valued pixel image
128 x 128 pixel array (ny, nx)
enclosing rectangle: [2667.538, 55941.94] x [21448.47, 50256.33] units
dimensions of each pixel: 416 x 225.0614 units
Image is defined on a subset of the rectangular grid
Subset area = 669941961.12249 square units
Subset area fraction = 0.437
Pixel values (inside window):
    range = [-6.584123e-21, 3.063698e-05]
    integral = 1927.788
    mean = 2.877545e-06
bw <- bw.diggle(childcareSG_ppp)
bw
   sigma 
295.9712 
childcareSG_ppp_km <- rescale.ppp(
  childcareSG_ppp, 1000, "km")
kde_childcareSG_km <- density(childcareSG_ppp_km,
                              sigma=bw.diggle,
                              edge=TRUE,
                              kernel="gaussian")
plot(kde_childcareSG_km)

bw.CvL(childcareSG_ppp_km)
   sigma 
4.357209 
bw.scott(childcareSG_ppp_km)
 sigma.x  sigma.y 
2.159749 1.396455 
bw.ppl(childcareSG_ppp_km)
   sigma 
0.378997 
bw.diggle(childcareSG_ppp_km)
    sigma 
0.2959712 
kde_childcareSG.ppl <- density(childcareSG_ppp_km, 
                               sigma=bw.ppl, 
                               edge=TRUE,
                               kernel="gaussian")
par(mfrow=c(1,2))
plot(kde_childcareSG_km, main = "bw.diggle")
plot(kde_childcareSG.ppl, main = "bw.ppl")

par(mfrow=c(2,2))
plot(density(childcareSG_ppp_km, 
             sigma=0.2959712, 
             edge=TRUE, 
             kernel="gaussian"), 
     main="Gaussian")
plot(density(childcareSG_ppp_km, 
             sigma=0.2959712, 
             edge=TRUE, 
             kernel="epanechnikov"), 
     main="Epanechnikov")
plot(density(childcareSG_ppp_km, 
             sigma=0.2959712, 
             edge=TRUE, 
             kernel="quartic"), 
     main="Quartic")
plot(density(childcareSG_ppp_km, 
             sigma=0.2959712, 
             edge=TRUE, 
             kernel="disc"), 
     main="Disc")

kde_childcareSG_fb <- density(childcareSG_ppp_km,
                              sigma=0.6, 
                              edge=TRUE,
                              kernel="gaussian")
plot(kde_childcareSG_fb)

kde_childcareSG_ab <- adaptive.density(
  childcareSG_ppp_km, 
  method="kernel")
plot(kde_childcareSG_ab)

par(mfrow=c(1,2))
plot(kde_childcareSG_fb, main = "Fixed bandwidth")
plot(kde_childcareSG_ab, main = "Adaptive bandwidth")

kde_childcareSG_bw_terra <- rast(kde_childcareSG_km)
class(kde_childcareSG_bw_terra)
[1] "SpatRaster"
attr(,"package")
[1] "terra"
kde_childcareSG_bw_terra
class       : SpatRaster 
size        : 128, 128, 1  (nrow, ncol, nlyr)
resolution  : 0.4162063, 0.2250614  (x, y)
extent      : 2.667538, 55.94194, 21.44847, 50.25633  (xmin, xmax, ymin, ymax)
coord. ref. :  
source(s)   : memory
name        :         lyr.1 
min value   : -5.824417e-15 
max value   :  3.063698e+01 
unit        :            km 
crs(kde_childcareSG_bw_terra) <- "EPSG:3414"
kde_childcareSG_bw_terra
class       : SpatRaster 
size        : 128, 128, 1  (nrow, ncol, nlyr)
resolution  : 0.4162063, 0.2250614  (x, y)
extent      : 2.667538, 55.94194, 21.44847, 50.25633  (xmin, xmax, ymin, ymax)
coord. ref. : SVY21 / Singapore TM (EPSG:3414) 
source(s)   : memory
name        :         lyr.1 
min value   : -5.824417e-15 
max value   :  3.063698e+01 
unit        :            km 
tm_shape(kde_childcareSG_bw_terra) + 
  tm_raster(col.scale = 
              tm_scale_continuous(
                values = "viridis"),
            col.legend = tm_legend(
            title = "Density values",
            title.size = 0.7,
            text.size = 0.7,
            bg.color = "white",
            bg.alpha = 0.7,
            position = tm_pos_in(
              "right", "bottom"),
            frame = TRUE)) +
  tm_graticules(labels.size = 0.7) +
  tm_compass() +
  tm_layout(scale = 1.0)

#Geospatial Data wrangling
pg <- mpsz_cl %>%
  filter(PLN_AREA_N == "PUNGGOL")
tm <- mpsz_cl %>%
  filter(PLN_AREA_N == "TAMPINES")
ck <- mpsz_cl %>%
  filter(PLN_AREA_N == "CHOA CHU KANG")
jw <- mpsz_cl %>%
  filter(PLN_AREA_N == "JURONG WEST")
par(mfrow=c(2,2))
plot(st_geometry(pg), main = "Ponggol")
plot(st_geometry(tm), main = "Tampines")
plot(st_geometry(ck), main = "Choa Chu Kang")
plot(st_geometry(jw), main = "Jurong West")

pg_owin = as.owin(pg)
tm_owin = as.owin(tm)
ck_owin = as.owin(ck)
jw_owin = as.owin(jw)
childcare_pg_ppp = childcare_ppp[pg_owin]
childcare_tm_ppp = childcare_ppp[tm_owin]
childcare_ck_ppp = childcare_ppp[ck_owin]
childcare_jw_ppp = childcare_ppp[jw_owin]
childcare_pg_ppp.km = rescale.ppp(childcare_pg_ppp, 1000, "km")
childcare_tm_ppp.km = rescale.ppp(childcare_tm_ppp, 1000, "km")
childcare_ck_ppp.km = rescale.ppp(childcare_ck_ppp, 1000, "km")
childcare_jw_ppp.km = rescale.ppp(childcare_jw_ppp, 1000, "km")
par(mfrow=c(2,2))
plot(unmark(childcare_pg_ppp.km), 
  main="Punggol")
plot(unmark(childcare_tm_ppp.km), 
  main="Tampines")
plot(unmark(childcare_ck_ppp.km), 
  main="Choa Chu Kang")
plot(unmark(childcare_jw_ppp.km), 
  main="Jurong West")

clarkevans.test(childcare_ck_ppp,
                correction="none",
                clipregion=NULL,
                alternative=c("two.sided"),
                nsim=999)

    Clark-Evans test
    No edge correction
    Z-test

data:  childcare_ck_ppp
R = 0.84097, p-value = 0.008866
alternative hypothesis: two-sided
clarkevans.test(childcare_tm_ppp,
                correction="none",
                clipregion=NULL,
                alternative=c("two.sided"),
                nsim=999)

    Clark-Evans test
    No edge correction
    Z-test

data:  childcare_tm_ppp
R = 0.66817, p-value = 6.58e-12
alternative hypothesis: two-sided
par(mfrow=c(2,2))
plot(density(childcare_pg_ppp.km, 
             sigma=bw.diggle, 
             edge=TRUE, 
             kernel="gaussian"),
     main="Punggol")
plot(density(childcare_tm_ppp.km, 
             sigma=bw.diggle, 
             edge=TRUE, 
             kernel="gaussian"),
     main="Tempines")
plot(density(childcare_ck_ppp.km, 
             sigma=bw.diggle, 
             edge=TRUE, 
             kernel="gaussian"),
     main="Choa Chu Kang")
plot(density(childcare_jw_ppp.km, 
             sigma=bw.diggle, 
             edge=TRUE, 
             kernel="gaussian"),
     main="Jurong West")

5.1 Overview

This is hands-on Exercise 2 , 5.1

Getting started

pacman::p_load(sf, spatstat, tmap, tidyverse, rvest, purrr)
setwd("C:/Users/HT Chen/Desktop/ISSS626/ISSS626-wp/Hands-on_Ex/Hands-on_Ex02")
mpsz_sf <- st_read("data/geospatial/MasterPlan2019SubzoneBoundaryNoSeaKML (1).kml") %>% 
  st_zm(drop = TRUE, what = "ZM") %>% st_transform(crs = 3414)
Reading layer `URA_MP19_SUBZONE_NO_SEA_PL' from data source 
  `C:\Users\HT Chen\Desktop\ISSS626\ISSS626-wp\Hands-on_Ex\Hands-on_Ex02\data\geospatial\MasterPlan2019SubzoneBoundaryNoSeaKML (1).kml' 
  using driver `KML'
Simple feature collection with 332 features and 2 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 103.6057 ymin: 1.158699 xmax: 104.0885 ymax: 1.470775
Geodetic CRS:  WGS 84
extract_kml_field <- function(html_text, field_name) {
  if (is.na(html_text) || html_text == "") return(NA_character_)
  
  page <- read_html(html_text)
  rows <- page %>% html_elements("tr")
  
  value <- rows %>%
    keep(~ html_text2(html_element(.x, "th")) == field_name) %>%
    html_element("td") %>%
    html_text2()
  
  if (length(value) == 0) NA_character_ else value
}
mpsz_sf <- mpsz_sf %>%
  mutate(
    REGION_N = map_chr(Description, extract_kml_field, "REGION_N"),
    PLN_AREA_N = map_chr(Description, extract_kml_field, "PLN_AREA_N"),
    SUBZONE_N = map_chr(Description, extract_kml_field, "SUBZONE_N"),
    SUBZONE_C = map_chr(Description, extract_kml_field, "SUBZONE_C")
  ) %>%
  select(-Name, -Description) %>%
  relocate(geometry, .after = last_col())
mpsz_cl <- mpsz_sf %>%
  filter(SUBZONE_N != "SOUTHERN GROUP",
         PLN_AREA_N != "WESTERN ISLANDS",
         PLN_AREA_N != "NORTH-EASTERN ISLANDS")
setwd("C:/Users/HT Chen/Desktop/ISSS626/ISSS626-wp/Hands-on_Ex/Hands-on_Ex02")
write_rds(mpsz_cl, "data/mpsz_cl_01.rds")
sg_owin <- as.owin(mpsz_cl)
class(sg_owin)
[1] "owin"
plot(sg_owin)

childcareSG_ppp = childcare_ppp[sg_owin]
childcareSG_ppp
Marked planar point pattern: 1925 points
Mark variables: Name, Description 
window: polygonal boundary
enclosing rectangle: [2667.54, 55941.94] x [21448.47, 50256.33] units
pg <- mpsz_cl %>%
  filter(PLN_AREA_N == "PUNGGOL")
tm <- mpsz_cl %>%
  filter(PLN_AREA_N == "TAMPINES")
ck <- mpsz_cl %>%
  filter(PLN_AREA_N == "CHOA CHU KANG")
jw <- mpsz_cl %>%
  filter(PLN_AREA_N == "JURONG WEST")
par(mfrow=c(2,2))
plot(st_geometry(pg), main = "Ponggol")
plot(st_geometry(tm), main = "Tampines")
plot(st_geometry(ck), main = "Choa Chu Kang")
plot(st_geometry(jw), main = "Jurong West")

pg_owin = as.owin(pg)
tm_owin = as.owin(tm)
ck_owin = as.owin(ck)
jw_owin = as.owin(jw)
childcare_pg_ppp = childcare_ppp[pg_owin]
childcare_tm_ppp = childcare_ppp[tm_owin]
childcare_ck_ppp = childcare_ppp[ck_owin]
childcare_jw_ppp = childcare_ppp[jw_owin]
childcare_pg_ppp.km = rescale.ppp(childcare_pg_ppp, 1000, "km")
childcare_tm_ppp.km = rescale.ppp(childcare_tm_ppp, 1000, "km")
childcare_ck_ppp.km = rescale.ppp(childcare_ck_ppp, 1000, "km")
childcare_jw_ppp.km = rescale.ppp(childcare_jw_ppp, 1000, "km")
par(mfrow=c(2,2))
plot(unmark(childcare_pg_ppp.km), 
  main="Punggol")
plot(unmark(childcare_tm_ppp.km), 
  main="Tampines")
plot(unmark(childcare_ck_ppp.km), 
  main="Choa Chu Kang")
plot(unmark(childcare_jw_ppp.km), 
  main="Jurong West")

set.seed(1234)
G_CK = Gest(childcare_ck_ppp, correction = "border")
plot(G_CK, xlim=c(0,500))

G_CK.csr <- envelope(childcare_ck_ppp, Gest, nsim = 999)
Generating 999 simulations of CSR  ...
1, 2, 3, ......10.........20.........30.........40.........50.........60..
.......70.........80.........90.........100.........110.........120.........130
.........140.........150.........160.........170.........180.........190........
.200.........210.........220.........230.........240.........250.........260......
...270.........280.........290.........300.........310.........320.........330....
.....340.........350.........360.........370.........380.........390.........400..
.......410.........420.........430.........440.........450.........460.........470
.........480.........490.........500.........510.........520.........530........
.540.........550.........560.........570.........580.........590.........600......
...610.........620.........630.........640.........650.........660.........670....
.....680.........690.........700.........710.........720.........730.........740..
.......750.........760.........770.........780.........790.........800.........810
.........820.........830.........840.........850.........860.........870........
.880.........890.........900.........910.........920.........930.........940......
...950.........960.........970.........980.........990........
999.

Done.
plot(G_CK.csr)

G_tm = Gest(childcare_tm_ppp, correction = "best")
plot(G_tm)

G_tm.csr <- envelope(childcare_tm_ppp, Gest, correction = "all", nsim = 999)
Generating 999 simulations of CSR  ...
1, 2, 3, ......10.........20.........30.........40.........50.........60..
.......70.........80.........90.........100.........110.........120.........130
.........140.........150.........160.........170.........180.........190........
.200.........210.........220.........230.........240.........250.........260......
...270.........280.........290.........300.........310.........320.........330....
.....340.........350.........360.........370.........380.........390.........400..
.......410.........420.........430.........440.........450.........460.........470
.........480.........490.........500.........510.........520.........530........
.540.........550.........560.........570.........580.........590.........600......
...610.........620.........630.........640.........650.........660.........670....
.....680.........690.........700.........710.........720.........730.........740..
.......750.........760.........770.........780.........790.........800.........810
.........820.........830.........840.........850.........860.........870........
.880.........890.........900.........910.........920.........930.........940......
...950.........960.........970.........980.........990........
999.

Done.
plot(G_tm.csr)

F_CK = Fest(childcare_ck_ppp)
plot(F_CK)

F_CK.csr <- envelope(childcare_ck_ppp, Fest, nsim = 999)
Generating 999 simulations of CSR  ...
1, 2, 3, ......10.........20.........30.........40.........50.........60..
.......70.........80.........90.........100.........110.........120.........130
.........140.........150.........160.........170.........180.........190........
.200.........210.........220.........230.........240.........250.........260......
...270.........280.........290.........300.........310.........320.........330....
.....340.........350.........360.........370.........380.........390.........400..
.......410.........420.........430.........440.........450.........460.........470
.........480.........490.........500.........510.........520.........530........
.540.........550.........560.........570.........580.........590.........600......
...610.........620.........630.........640.........650.........660.........670....
.....680.........690.........700.........710.........720.........730.........740..
.......750.........760.........770.........780.........790.........800.........810
.........820.........830.........840.........850.........860.........870........
.880.........890.........900.........910.........920.........930.........940......
...950.........960.........970.........980.........990........
999.

Done.
plot(F_CK.csr)

F_tm = Fest(childcare_tm_ppp, correction = "best")
plot(F_tm)

F_tm.csr <- envelope(childcare_tm_ppp, Fest, correction = "all", nsim = 999)
Generating 999 simulations of CSR  ...
1, 2, 3, ......10.........20.........30.........40.........50.........60..
.......70.........80.........90.........100.........110.........120.........130
.........140.........150.........160.........170.........180.........190........
.200.........210.........220.........230.........240.........250.........260......
...270.........280.........290.........300.........310.........320.........330....
.....340.........350.........360.........370.........380.........390.........400..
.......410.........420.........430.........440.........450.........460.........470
.........480.........490.........500.........510.........520.........530........
.540.........550.........560.........570.........580.........590.........600......
...610.........620.........630.........640.........650.........660.........670....
.....680.........690.........700.........710.........720.........730.........740..
.......750.........760.........770.........780.........790.........800.........810
.........820.........830.........840.........850.........860.........870........
.880.........890.........900.........910.........920.........930.........940......
...950.........960.........970.........980.........990........
999.

Done.
plot(F_tm.csr)

K_ck = Kest(childcare_ck_ppp, correction = "Ripley")
plot(K_ck, . -r ~ r, ylab= "K(d)-r", xlab = "d(m)")

K_ck.csr <- envelope(childcare_tm_ppp, Fest, correction = "all", nsim = 999)
Generating 999 simulations of CSR  ...
1, 2, 3, ......10.........20.........30.........40.........50.........60..
.......70.........80.........90.........100.........110.........120.........130
.........140.........150.........160.........170.........180.........190........
.200.........210.........220.........230.........240.........250.........260......
...270.........280.........290.........300.........310.........320.........330....
.....340.........350.........360.........370.........380.........390.........400..
.......410.........420.........430.........440.........450.........460.........470
.........480.........490.........500.........510.........520.........530........
.540.........550.........560.........570.........580.........590.........600......
...610.........620.........630.........640.........650.........660.........670....
.....680.........690.........700.........710.........720.........730.........740..
.......750.........760.........770.........780.........790.........800.........810
.........820.........830.........840.........850.........860.........870........
.880.........890.........900.........910.........920.........930.........940......
...950.........960.........970.........980.........990........
999.

Done.
plot(K_ck.csr, . - r ~ r, xlab="d", ylab="K(d)-r")

K_tm = Kest(childcare_tm_ppp, correction = "Ripley")
plot(K_tm, . -r ~ r, 
     ylab= "K(d)-r", xlab = "d(m)", 
     xlim=c(0,1000))

K_tm.csr <- envelope(childcare_tm_ppp, Kest, nsim = 99, rank = 1, glocal=TRUE)
Generating 99 simulations of CSR  ...
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,
41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 
99.

Done.
plot(K_tm.csr, . - r ~ r, 
     xlab="d", ylab="K(d)-r", xlim=c(0,500))

L_ck = Lest(childcare_ck_ppp, correction = "Ripley")
plot(L_ck, . -r ~ r, 
     ylab= "L(d)-r", xlab = "d(m)")

L_ck.csr <- envelope(childcare_ck_ppp, Lest, nsim = 99, rank = 1, glocal=TRUE)
Generating 99 simulations of CSR  ...
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,
41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 
99.

Done.
plot(L_ck.csr, . - r ~ r, xlab="d", ylab="L(d)-r")

L_tm = Lest(childcare_tm_ppp, correction = "Ripley")
plot(L_tm, . -r ~ r, 
     ylab= "L(d)-r", xlab = "d(m)", 
     xlim=c(0,1000))

L_tm.csr <- envelope(childcare_tm_ppp, Lest, nsim = 99, rank = 1, glocal=TRUE)
Generating 99 simulations of CSR  ...
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,
41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 
99.

Done.
plot(L_tm.csr, . - r ~ r, 
     xlab="d", ylab="L(d)-r", xlim=c(0,500))